Описание: Offering a clear set of workable examples with data and explanations, Interaction Effects in Linear and Generalized Linear Models is a comprehensive and accessible text that provides a unified approach to interpreting interaction effects.
Автор: Lee, Youngjo (Seoul National University, South Korea) Nelder, John A. Pawitan, Yudi (Karolinska Institute, Stockholm, Sweden) Название: Generalized Linear Models with Random Effects ISBN: 1032096632 ISBN-13(EAN): 9781032096636 Издательство: Taylor&Francis Рейтинг: Цена: 7501.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This is the second edition of a monograph on generalized linear models with random effects that extends the classic work of McCullagh and Nelder. It has been thoroughly updated, with around 80 pages added, including new material on the extended likelihood approach that strengthens the theoretical basis of the methodology, new developments in var
Автор: Faraway, Julian J. (university Of Bath, United Kingdom) Название: Extending the linear model with r ISBN: 149872096X ISBN-13(EAN): 9781498720960 Издательство: Taylor&Francis Рейтинг: Цена: 14086.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Автор: W. Hennevogl; Ludwig Fahrmeir; Gerhard Tutz Название: Multivariate Statistical Modelling Based on Generalized Linear Models ISBN: 1441929002 ISBN-13(EAN): 9781441929006 Издательство: Springer Рейтинг: Цена: 27251.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The book is aimed at applied statisticians, graduate students of statistics, and students and researchers with a strong interest in statistics and data analysis. This second edition is extensively revised, especially those sections relating with Bayesian concepts.
Автор: Dobson, Annette J. (university Of Queensland, Herston, Australia) Barnett, Adrian (queensland University Of Technology, Kelvin Grove, Australia) Название: Introduction to generalized linear models, fourth edition ISBN: 1138741515 ISBN-13(EAN): 9781138741515 Издательство: Taylor&Francis Рейтинг: Цена: 5808.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: An Introduction to Generalized Linear Models, Fourth Edition provides a cohesive framework for statistical modelling, with an emphasis on numerical and graphical methods. This new edition of a bestseller has been updated with new sections on non-linear associations, strategies for model selection, and a Postface on good statistical practice.
Автор: Dobson Название: An Introduction to Generalized Linear Models ISBN: 113874168X ISBN-13(EAN): 9781138741683 Издательство: Taylor&Francis Рейтинг: Цена: 25265.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: An Introduction to Generalized Linear Models, Fourth Edition provides a cohesive framework for statistical modelling, with an emphasis on numerical and graphical methods. This new edition of a bestseller has been updated with new sections on non-linear associations, strategies for model selection, and a Postface on good statistical practice.
Автор: Gill, Jefferson M. Torres Pacheco, Silvia Michelle Название: Generalized linear models ISBN: 1506387349 ISBN-13(EAN): 9781506387345 Издательство: Sage Publications Рейтинг: Цена: 5859.00 р. Наличие на складе: Поставка под заказ.
Описание: Explaining the theoretical underpinning of generalized linear models, this text enables researchers to decide how to select the best way to adapt their data for this type of analysis, with examples to illustrate the application of GLM.
Автор: Robert Gilchrist; Brian Francis; Joe Whittaker Название: Generalized Linear Models ISBN: 0387962247 ISBN-13(EAN): 9780387962245 Издательство: Springer Рейтинг: Цена: 16769.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Автор: Dunn Название: Generalized linear models with examples ISBN: 1441901175 ISBN-13(EAN): 9781441901170 Издательство: Springer Рейтинг: Цена: 15372.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This textbook presents an introduction to generalized linear models, complete with real-world data sets and practice problems, making it applicable for both beginning and advanced students of applied statistics.
Описание: Edward Vonesh's Generalized Linear and Nonlinear Models for Correlated Data: Theory and Applications Using SAS is devoted to the analysis of correlated response data using SAS, with special emphasis on applications that require the use of generalized linear models or generalized nonlinear models. Written in a clear, easy-to-understand manner, it provides applied statisticians with the necessary theory, tools, and understanding to conduct complex analyses of continuous and/or discrete correlated data in a longitudinal or clustered data setting. Using numerous and complex examples, the book emphasizes real-world applications where the underlying model requires a nonlinear rather than linear formulation and compares and contrasts the various estimation techniques for both marginal and mixed-effects models. The SAS procedures MIXED, GENMOD, GLIMMIX, and NLMIXED as well as user-specified macros will be used extensively in these applications. In addition, the book provides detailed software code with most examples so that readers can begin applying the various techniques immediately.
Автор: Lee, Youngjo Ronnegard, Lars Noh, Maengseok Название: Data analysis using hierarchical generalized linear models with r ISBN: 0367657929 ISBN-13(EAN): 9780367657925 Издательство: Taylor&Francis Рейтинг: Цена: 7348.00 р. Наличие на складе: Нет в наличии.
Описание: Since their introduction, hierarchical generalized linear models (HGLMs) have proven useful in various fields by allowing random effects in regression models. Interest in the topic has grown, and various practical analytical tools have been developed. This book summarizes developments within the field and, using data examples, illustrates how to
Описание: This book covers two major classes of mixed effects models, linear mixed models and generalized linear mixed models. Furthermore, it includes recently developed methods, such as mixed model diagnostics, mixed model selection, and jackknife method in the context of mixed models.
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